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We present a detailed near-infrared study of an embedded cluster located in the hub of the giant molecular cloud G148.24+00.41 of mass $\sim$10$^5$ $M_\odot$, with the TANSPEC instrument mounted on the 3.6 m Devasthal Optical Telescope. The hub is located near the geometric center of the cloud and represents its most massive clump. We studied the central 2 pc $\times$ 2 pc area of the hub with 5$\sigma$ limiting magnitudes of 20.5, 20.1, and 18.6 mag in the $J$, $H$, and $K_s$ bands, respectively. Using the $K_s$-band luminosity function and comparing it with the synthetic clusters, we obtained the age of the cluster as $\sim$0.5 Myr, which was found to corroborate well with the visual extinction versus the age of nearby embedded clusters. We find that the present mass of the cluster is around $\sim$180 $M_\odot$, and the cluster is currently forming stars at a rate of $\sim$330 $M_\odot$ $\rm{Myr}^{-1}$, with an efficiency of $\sim$20%. The cluster is connected to an extended gas reservoir through a filamentary network; thus, we hypothesize that the cluster has the potential to become a richer cluster in a few Myr of time., Comment: 17 pages and 13 figures

Cygnus X-1 is a high mass X-ray binary where accretion onto the black hole is mediated by the stellar wind from the blue supergiant companion star HDE 226868. Depending on the position of the black hole along the orbit, X-ray observations can probe different layers of the stellar wind. Deeper wind layers can be investigated at superior conjunction (i.e. null orbital phases). We aim at characterising the stellar wind in the Cyg X-1/HDE 226868 system analysing one passage at superior conjunction covered by XMM-Newton during the CHOCBOX campaign via modelling of colour-colour diagrams. Since X-ray absorption is energy-dependent, colour indices provide information on the parameters of the stellar wind, such as the column density $N_{H,w}$ and the covering factor $f_c$. We fitted colour-colour diagrams with models that include both a continuum and a stellar wind component. We used the KDE method to infer the unknown probability distribution of the data points in the colour-colour diagram, and selected the model corresponding to the highest likelihood. In order to study the temporal evolution of the wind around superior conjunction, we extracted and fitted time-resolved colour-colour diagrams. We found that the model that best describes the shape of the colour-colour diagram of Cyg X-1 at superior conjunction requires the wind to be partially ionised. The shape of the colour-colour diagram strongly varies during the analysed observation, as due to concurrent changes of the mean $N_{H,w}$ and the $f_c$ of the wind. Our results suggest the existence of a linear scaling between the rapid variability amplitude of $N_{H,w}$ (on time scales between 10 s and 11 ks) and its long term variations (on time scales 11>ks). Using the inferred best-fit values, we estimated the stellar mass loss rate to be $\sim 7\times10^{-6} {\rm M_{\odot}yr^{-1}}$ and the clumps to have a mass of $\sim10^{17}$ g., Comment: Accepted for publication in A&A

Layered van der Waals two-dimensional (2D) magnets are a cornerstone of ultrathin spintronic and magnonic devices. The recent discovery of a 2D multiferroic with strong magnetoelectric coupling in NiI$_2$ offers a promising platform for the electrical control of spin-wave transport. In this work, using ab initio calculations, we investigate how the magnonic properties of monolayer NiI$_2$ can be controlled using an external electric field. We show that the emergence of a ferroelectric polarization leads to an energy splitting in the magnon spectrum, thus establishing a way to detect the electric polarization experimentally. We also show the modulation of the magnon splitting and the energy position of the singularities in magnon DOS by an electric field due to the strong magnetoelectric coupling. Our results highlight the interplay between ferroelectricity and magnons in van der Waals multiferroics and pave the way to design electrically tunable magnetic devices at the 2D limit.

Text Recognition (TR) refers to the research area that focuses on retrieving textual information from images, a topic that has seen significant advancements in the last decade due to the use of Deep Neural Networks (DNN). However, these solutions often necessitate vast amounts of manually labeled or synthetic data. Addressing this challenge, Self-Supervised Learning (SSL) has gained attention by utilizing large datasets of unlabeled data to train DNN, thereby generating meaningful and robust representations. Although SSL was initially overlooked in TR because of its unique characteristics, recent years have witnessed a surge in the development of SSL methods specifically for this field. This rapid development, however, has led to many methods being explored independently, without taking previous efforts in methodology or comparison into account, thereby hindering progress in the field of research. This paper, therefore, seeks to consolidate the use of SSL in the field of TR, offering a critical and comprehensive overview of the current state of the art. We will review and analyze the existing methods, compare their results, and highlight inconsistencies in the current literature. This thorough analysis aims to provide general insights into the field, propose standardizations, identify new research directions, and foster its proper development., Comment: This article is under revision

In this work, we uncover a collection of non invertible topological operators linked to the 0-, 2-, 4- and 6-form symmetries arising from the type IIB superstring effective pseudo action. By pinpointing the $\text{SL}(2,\mathbb{R})$-covariant conserved currents corresponding to these symmetries, we first derive a set of $\text{SL}(2,\mathbb{Z})$ invariant invertible topological operators that encapsulate the integer BPS charges inherent to the theory. Moving forward, by incorporating fractional charges while maintaining gauge invariance, we introduce the non invertible topological operators for each generalized symmetry, and in particular for the $\text{SL}(2,\mathbb{R})$ 0-form symmetry. Identifying them as a novel kind of symmetries reminiscent of fractional quantum Hall effect (FQHE)-like non invertible operators, we study their action on charged objects., Comment: are welcome. 16 pages, 1 figure

The recent discoveries of Neptune-sized ultra-short period planets (USPs) challenge existing planet formation theories. It is unclear whether these residents of the Hot Neptune Desert have similar origins to smaller, rocky USPs, or if this discrete population is evidence of a different formation pathway altogether. We report the discovery of TOI-3261b, an ultra-hot Neptune with an orbital period $P$ = 0.88 days. The host star is a $V = 13.2$ magnitude, slightly super-solar metallicity ([Fe/H] $\simeq$ 0.15), inactive K1.5 main sequence star at $d = 300$ pc. Using data from the Transiting Exoplanet Survey Satellite and the Las Cumbres Observatory Global Telescope, we find that TOI-3261b has a radius of $3.82_{-0.35}^{+0.42}$ $R_{\oplus}$. Moreover, radial velocities from ESPRESSO and HARPS reveal a mass of $30.3_{-2.4}^{+2.2}$ $M_{\oplus}$, more than twice the median mass of Neptune-sized planets on longer orbits. We investigate multiple mechanisms of mass loss that can reproduce the current-day properties of TOI-3261b, simulating the evolution of the planet via tidal stripping and photoevaporation. Thermal evolution models suggest that TOI-3261b should retain an envelope potentially enriched with volatiles constituting $\sim$5% of its total mass. This is the second highest envelope mass fraction among ultra-hot Neptunes discovered to date, making TOI-3261b an ideal candidate for atmospheric follow-up observations., Comment: 20 pages, 11 figures, accepted to AJ

We consider massive type IIA compactifications down to 4 dimensions in presence of O6 planes and D6 branes parallel to them, in order to preserve half-maximal supersymmetry in 4D. The dynamics of open strings living on the spacetime filling branes is taken into account, in the gauged supergravity description, by adding extra vector multiplets and embedding tensor components. The scalar potential gets new terms that can be matched with contributions coming from dimensional reduction of the non-Abelian DBI and WZ brane actions. In this setting, we analyze the vacuum structure of the theory and find novel AdS$_4$ vacua, both supersymmetric and non-supersymmetric ones. Furthermore, we address their perturbative stability by computing their mass spectra. Some of the vacua are found to be perturbatively stable, despite their being non-supersymmetric. We conclude by discussing the reliability of our setup in terms of higher-derivative corrections., Comment: 39 pages

Air pollution and greenhouse emissions are a significant problem across various sectors, urging the need for advanced technologies to detect and capture harmful gases. In recent years, two-dimensional (2D) materials have attracted an increasing attention due to their large surface-to-volume ratio and reactivity. Herein, we investigate the potential of single-layer CrSBr for gas sensing and capturing by means of first-principles calculations. We explore the adsorption behaviour of different pollutant gases (H$_2$S, NH$_3$, NO, NO$_2$, CO and CO$_2$) on this 2D ferromagnet and the impact of intrinsic defects on its magnetic properties. Interestingly, we find that Br vacancies enhance the adsorption of NH$_3$, NO and NO$_2$ and induces a selective frequency shift on the magnon dispersion. This work motivates the creation of novel magnonic gas sensing devices based on 2D van der Waals magnetic materials., Comment: 13 pages, 5 figures, 1 table

Collaborative robots or cobots interact with humans in a common work environment. In cobots, one under investigated but important issue is related to their movement and how it is perceived by humans. This paper tries to analyze whether humans prefer a robot moving in a human or in a robotic fashion. To this end, the present work lays out what differentiates the movement performed by an industrial robotic arm from that performed by a human one. The main difference lies in the fact that the robotic movement has a trapezoidal speed profile, while for the human arm, the speed profile is bell-shaped and during complex movements, it can be considered as a sum of superimposed bell-shaped movements. Based on the lognormality principle, a procedure was developed for a robotic arm to perform human-like movements. Both speed profiles were implemented in two industrial robots, namely, an ABB IRB 120 and a Universal Robot UR3. Three tests were used to study the subjects' preference when seeing both movements and another analyzed the same when interacting with the robot by touching its ends with their fingers.

We study the topological Weyl type-II semimetal WTe$_2$ via Scanning Tunneling Microscopy (STM) and Density Functional Theory calculations (DFT). We succesfully determine the band structure at zero field close to the Fermi level by comparing quasiparticle interference (QPI) experiments with DFT. Under magnetic fields, we observe Landau level quantization on atomic scale measurements and find a level sequence evidencing a linearly dispersing portion of the band structure. Our results establish the long sought connection between Weyl cones and Landau quantization in WTe$_2$. Atomic scale Landau quantization emerges as a powerful probe of topological surface states for semimetals, superconductors and topological insulators., Comment: 17 pages, 11 figures

Multiple imputation (MI) models can be improved by including auxiliary covariates (AC), but their performance in high-dimensional data is not well understood. We aimed to develop and compare high-dimensional MI (HDMI) approaches using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a plasmode simulation study using data from opioid vs. non-steroidal anti-inflammatory drug (NSAID) initiators (X) with observed serum creatinine labs (Z2) and time-to-acute kidney injury as outcome. We simulated 100 cohorts with a null treatment effect, including X, Z2, atrial fibrillation (U), and 13 other investigator-derived confounders (Z1) in the outcome generation. We then imposed missingness (MZ2) on 50% of Z2 measurements as a function of Z2 and U and created different HDMI candidate AC using structured and NLP-derived features. We mimicked scenarios where U was unobserved by omitting it from all AC candidate sets. Using LASSO, we data-adaptively selected HDMI covariates associated with Z2 and MZ2 for MI, and with U to include in propensity score models. The treatment effect was estimated following propensity score matching in MI datasets and we benchmarked HDMI approaches against a baseline imputation and complete case analysis with Z1 only. HDMI using claims data showed the lowest bias (0.072). Combining claims and sentence embeddings led to an improvement in the efficiency displaying the lowest root-mean-squared-error (0.173) and coverage (94%). NLP-derived AC alone did not perform better than baseline MI. HDMI approaches may decrease bias in studies with partially observed confounders where missingness depends on unobserved factors.

The real estate market shows an inherent connection to space. Real estate agencies unevenly operate and specialize across space, price and type of properties, thereby segmenting the market into submarkets. We introduce here a methodology based on multipartite networks to detect the spatial segmentation emerging from data on housing online listings. Considering the spatial information of the listings, we build a bipartite network that connects agencies and spatial units. This bipartite network is projected into a network of spatial units, whose connections account for similarities in the agency ecosystem. We then apply clustering methods to this network to segment markets into spatially-coherent regions, which are found to be robust across different clustering detection algorithms, discretization of space and spatial scales, and across countries with case studies in France and Spain. This methodology addresses the long-standing issue of housing market segmentation, relevant in disciplines such as urban studies and spatial economics, and with implications for policymaking., Comment: 9 pages, 10 figures

In this paper we compute the Chermak-Delgado measure of the mod $p^{n}$ Heisenberg Group for any prime $p$. To achieve this we introduce the notion of the pseudocentralizer and prove various results about it., Comment: 40 pages

Sensors and Artificial Intelligence (AI) have revolutionized the analysis of human movement, but the scarcity of specific samples presents a significant challenge in training intelligent systems, particularly in the context of diagnosing neurodegenerative diseases. This study investigates the feasibility of utilizing robot-collected data to train classification systems traditionally trained with human-collected data. As a proof of concept, we recorded a database of numeric characters using an ABB robotic arm and an Apple Watch. We compare the classification performance of the trained systems using both human-recorded and robot-recorded data. Our primary objective is to determine the potential for accurate identification of human numeric characters wearing a smartwatch using robotic movement as training data. The findings of this study offer valuable insights into the feasibility of using robot-collected data for training classification systems. This research holds broad implications across various domains that require reliable identification, particularly in scenarios where access to human-specific data is limited.

We initiate the LISA template databank for stochastic gravitational wave backgrounds sourced by cosmic strings. We include two templates, an analytical template, which enables more flexible searches, and a numerical template derived directly from large Nambu-Goto simulations of string networks. Using searches based on these templates, we forecast the parameter space within the reach of the experiment and the precision with which their parameters will be reconstructed, provided a signal is observed. The reconstruction permits probing the Hubble expansion and new relativistic DoF in the early universe. We quantify the impact that astrophysical foregrounds can have on these searches. Finally, we discuss the impact that these observations would have on our understanding of the fundamental models behind the string networks. Overall, we prove that LISA has great potential for probing cosmic string models and may reach tensions as low as $G\mu =10^{-16} - 10^{-17} $, which translates into energy scales of the order $10^{11}~\text{GeV}$., Comment: 59 pages, 15 figures

In this paper we construct families of bit sequences using combinatorial methods. Each sequence is derived by con- verting a collection of numbers encoding certain combinatorial nu- merics from objects exhibiting symmetry in various dimensions. Using the algorithms first described in [1] we show that the NIST testing suite described in publication 800-22 does not detect these symmetries hidden within these sequences.

Visual electrophysiology is often used clinically to determine functional changes associated with retinal or neurological conditions. The full-field flash electroretinogram (ERG) assesses the global contribution of the outer and inner retinal layers initiated by the rods and cone pathways depending on the state of retinal adaptation. Within clinical centers reference normative data are used to compare with clinical cases that may be rare or underpowered within a specific demographic. To bolster either reference or case datasets the application of synthetic ERG waveforms may offer benefits to disease classification and case-control studies. In this study and as a proof of concept, artificial intelligence (AI) to generate synthetic signals using Generative Adversarial Networks is deployed to up-scale male participants within an ISCEV reference dataset containing 68 participants, with waveforms from the right and left eye. Random Forest Classifiers further improved classification for sex within the group from a balanced accuracy of 0.72 to 0.83 with the added synthetic male waveforms. This is the first study to demonstrate the generation of synthetic ERG waveforms to improve machine learning classification modelling with electroretinogram waveforms.

Handwritten Text Recognition (HTR) is a relevant problem in computer vision, and implies unique challenges owing to its inherent variability and the rich contextualization required for its interpretation. Despite the success of Self-Supervised Learning (SSL) in computer vision, its application to HTR has been rather scattered, leaving key SSL methodologies unexplored. This work focuses on one of them, namely Spatial Context-based SSL. We investigate how this family of approaches can be adapted and optimized for HTR and propose new workflows that leverage the unique features of handwritten text. Our experiments demonstrate that the methods considered lead to advancements in the state-of-the-art of SSL for HTR in a number of benchmark cases.

The ARS 548 RDI Radar is a premium model of the fifth generation of 77 GHz long range radar sensors with new RF antenna arrays, which offer digital beam forming. This radar measures independently the distance, speed and angle of objects without any reflectors in one measurement cycle based on Pulse Compression with New Frequency Modulation. Unfortunately, to the best of our knowledge, there are no open source drivers available for Linux systems to enable users to analyze the data acquired by the sensor. In this paper, we present a driver that can interpret the data from the ARS 548 RDI sensor and make it available over the Robot Operating System versions 1 and 2 (ROS and ROS2). Thus, these data can be stored, represented, and analyzed using the powerful tools offered by ROS. Besides, our driver offers advanced object features provided by the sensor, such as relative estimated velocity and acceleration of each object, its orientation and angular velocity. We focus on the configuration of the sensor and the use of our driver including its filtering and representation tools. Besides, we offer a video tutorial to help in its configuration process. Finally, a dataset acquired with this sensor and an Ouster OS1-32 LiDAR sensor, to have baseline measurements, is available, so that the user can check the correctness of our driver., Comment: 19 pages, 6 figures and 22 references

An outstanding mystery of dark matter physics is the lack of direct detection signals to date. We suggest that dark matter is scatophobic: due to a repulsive long-range interaction, it is repelled by objects with a large net scat charge, such as the Earth, and is therefore not able to reach direct detection experiments. This represents the first step in a broader theoretical paradigm that we dub the "anti-anthropic principle.", Comment: 8 pages, 4 of which are references, two figures. v2: added references

There are different proposals in the literature on how to protect pedestrians using warning systems to alert drivers of their presence. They can be based on onboard perception systems or wireless communications. The evaluation of these systems has been focused on testing their ability to detect pedestrians. A problem that has received much less attention is the possibility of generating too many alerts in the warning systems. In this paper, we propose and analyze four different algorithms to take the decision on generating alerts in a warning system that is based on direct wireless communications between vehicles and pedestrians. With the algorithms, we explore different strategies to reduce unnecessary alerts. The feasibility of the implementation of the algorithms was evaluated with a deployment using real equipment, and tests were carried out to verify their behavior in real scenarios. The ability of each algorithm to reduce unnecessary alerts was evaluated with realistic simulations in an urban scenario, using a traffic simulator with vehicular and pedestrian flows. The results show the importance of tackling the problem of driver overload in warning systems, and that it is not straightforward to predict the load of alerts generated by an algorithm in a large-scale deployment, in which there are multiple interactions between vehicles and pedestrians.

Recent advancements in 2D magnetic materials have attracted a growing interest driven by their unique properties and potential applications in spintronic devices. However, the scarcity of systems that exhibit magnetism at room-temperature has limited their practical implementation into functional devices. In this work we focus on the recently synthetised van der Waals (vdW) ferromagnet Fe$_3$GaTe$_2$, which exhibits above-room-temperature magnetism (T$_{\mathrm{c}}$ = 350-380 K) and strong perpendicular magnetic anisotropy. Through first-principles calculations, we examine the magnetic properties of Fe$_3$GaTe$_2$ and compare them with the widely known Fe$_3$GeTe$_2$ ferromagnet. Our calculations unveil the complex microscopic mechanisms governing their magnetic behaviour, emphasizing the pivotal role of the ferromagnetic in-plane exchange interactions in the stabilization of the elevated T$_{\mathrm{c}}$ in Fe$_3$GaTe$_2$. Additionally, we predict the stability, strong perpendicular anisotropy and high T$_{\mathrm{c}}$ of single-layer Fe$_3$GaTe$_2$. We also demonstrate the potential of strain engineering and electrostatic doping to modulate its magnetic exchange interactions and anisotropy. Our results incentivise the isolation of the monolayer and pave the way for the future optimization of Fe$_3$GaTe$_2$ in magnetic and spintronic nanodevices.

When considering airborne epidemic spreading in social systems, a natural connection arises between mobility and epidemic contacts. As individuals travel, possibilities to encounter new people either at the final destination or during the transportation process appear. Such contacts can lead to new contagion events. In fact, mobility has been a crucial target for early non-pharmaceutical containment measures against the recent COVID-19 pandemic, with a degree of intensity ranging from public transportation line closures to regional, city or even home confinements. Nonetheless, quantitative knowledge on the relationship between mobility-contagions and, consequently, on the efficiency of containment measures remains elusive. Here we introduce an agent-based model with a simple interaction between mobility and contacts. Despite its simplicity our model shows the emergence of a critical mobility level, inducing major outbreaks when surpassed. We explore the interplay between mobility restrictions and the infection in recent intervention policies seen across many countries, and how interventions in the form of closures triggered by incidence rates can guide the epidemic into an oscillatory regime with recurrent waves. We consider how the different interventions impact societal well-being, the economy and the population. Finally, we propose a mitigation framework based on the critical nature of mobility in an epidemic, able to suppress incidence and oscillations at will, preventing extreme incidence peaks with potential to saturate health care resources., Comment: 13 pages, 5 figures

Excitations between localized 3d states of transition metal ions within crystalline solids, commonly known as d-d transitions, play a pivotal role in diverse phenomena across solid state physics, materials science, and chemistry. These transitions contribute to the coloration in transition metal oxides, catalytic processes on oxide surfaces, and high-temperature superconductivity. They also couple optical excitation to quantized collective phenomena such as phonons and magnons in magnetic systems. Until now, an experimental method to unravel the complex quasiparticle dynamics associated with d-d transitions has remained elusive. We bridge this gap by demonstrating that d-d transitions can be distinctly traced in momentum space and time using time- and angle-resolved photoelectron spectroscopy (trARPES). Through this approach, we can assign specific momentum-dependent characteristics and elucidate the decay mechanisms of specific d-d transitions in FePS$_{3}$, a two-dimensional van der Waals antiferromagnet with a rich array of quantum phenomena stemming from d-d transitions. This study pioneers the use of ARPES in probing the dynamics of d-d transitions across a wide spectrum of solid-state systems., Comment: 15 pages, 4 figures

Catheter ablation (CA) remains the cornerstone alternative to cardioversion for sinus rhythm (SR) restoration in patients with atrial fibrillation (AF). Unfortunately, despite the last methodological and technological advances, this procedure is not consistently effective in treating persistent AF.

From a non-central panorama, 3D lines can be recovered by geometric reasoning. However, their sensitivity to noise and the complex geometric modeling required has led these panoramas being very little investigated. In this work we present a novel approach for 3D layout recovery of indoor environments using single non-central panoramas. We obtain the boundaries of the structural lines of the room from a non-central panorama using deep learning and exploit the properties of non-central projection systems in a new geometrical processing to recover the scaled layout. We solve the problem for Manhattan environments, handling occlusions, and also for Atlanta environments in an unified method. The experiments performed improve the state-of-the-art methods for 3D layout recovery from a single panorama. Our approach is the first work using deep learning with non-central panoramas and recovering the scale of single panorama layouts., Comment: arXiv admin note: substantial text overlap with arXiv:2401.17058

In this article we present a visual gyroscope based on equirectangular panoramas. We propose a new pipeline where we take advantage of combining three different methods to obtain a robust and accurate estimation of the attitude of the camera. We quantitatively and qualitatively validate our method on two image sequences taken with a $360^\circ$ dual-fisheye camera mounted on different aerial vehicles.

Convolution kernels are the basic structural component of convolutional neural networks (CNNs). In the last years there has been a growing interest in fisheye cameras for many applications. However, the radially symmetric projection model of these cameras produces high distortions that affect the performance of CNNs, especially when the field of view is very large. In this work, we tackle this problem by proposing a method that leverages the calibration of cameras to deform the convolution kernel accordingly and adapt to the distortion. That way, the receptive field of the convolution is similar to standard convolutions in perspective images, allowing us to take advantage of pre-trained networks in large perspective datasets. We show how, with just a brief fine-tuning stage in a small dataset, we improve the performance of the network for the calibrated fisheye with respect to standard convolutions in depth estimation and semantic segmentation., Comment: Previously presented at BMVC: https://proceedings.bmvc2023.org/721/

Omnidirectional images are one of the main sources of information for learning based scene understanding algorithms. However, annotated datasets of omnidirectional images cannot keep the pace of these learning based algorithms development. Among the different panoramas and in contrast to standard central ones, non-central panoramas provide geometrical information in the distortion of the image from which we can retrieve 3D information of the environment [2]. However, due to the lack of commercial non-central devices, up until now there was no dataset of these kinds of panoramas. In this data paper, we present the first dataset of non-central panoramas for indoor scene understanding. The dataset is composed by {\bf 2574} RGB non-central panoramas taken in around 650 different rooms. Each panorama has associated a depth map and annotations to obtain the layout of the room from the image as a structural edge map, list of corners in the image, the 3D corners of the room and the camera pose. The images are taken from photorealistic virtual environments and pixel-wise automatically annotated.

Omnidirectional and 360{\deg} images are becoming widespread in industry and in consumer society, causing omnidirectional computer vision to gain attention. Their wide field of view allows the gathering of a great amount of information about the environment from only an image. However, the distortion of these images requires the development of specific algorithms for their treatment and interpretation. Moreover, a high number of images is essential for the correct training of computer vision algorithms based on learning. In this paper, we present a tool for generating datasets of omnidirectional images with semantic and depth information. These images are synthesized from a set of captures that are acquired in a realistic virtual environment for Unreal Engine 4 through an interface plugin. We gather a variety of well-known projection models such as equirectangular and cylindrical panoramas, different fish-eye lenses, catadioptric systems, and empiric models. Furthermore, we include in our tool photorealistic non-central-projection systems as non-central panoramas and non-central catadioptric systems. As far as we know, this is the first reported tool for generating photorealistic non-central images in the literature. Moreover, since the omnidirectional images are made virtually, we provide pixel-wise information about semantics and depth as well as perfect knowledge of the calibration parameters of the cameras. This allows the creation of ground-truth information with pixel precision for training learning algorithms and testing 3D vision approaches. To validate the proposed tool, different computer vision algorithms are tested as line extractions from dioptric and catadioptric central images, 3D Layout recovery and SLAM using equirectangular panoramas, and 3D reconstruction from non-central panoramas.

In this work we present a novel approach for 3D layout recovery of indoor environments using a non-central acquisition system. From a non-central panorama, full and scaled 3D lines can be independently recovered by geometry reasoning without geometric nor scale assumptions. However, their sensitivity to noise and complex geometric modeling has led these panoramas being little investigated. Our new pipeline aims to extract the boundaries of the structural lines of an indoor environment with a neural network and exploit the properties of non-central projection systems in a new geometrical processing to recover an scaled 3D layout. The results of our experiments show that we improve state-of-the-art methods for layout reconstruction and line extraction in non-central projection systems. We completely solve the problem in Manhattan and Atlanta environments, handling occlusions and retrieving the metric scale of the room without extra measurements. As far as the authors knowledge goes, our approach is the first work using deep learning on non-central panoramas and recovering scaled layouts from single panoramas.

Finding obstacle-free paths in unknown environments is a big navigation issue for visually impaired people and autonomous robots. Previous works focus on obstacle avoidance, however they do not have a general view of the environment they are moving in. New devices based on computer vision systems can help impaired people to overcome the difficulties of navigating in unknown environments in safe conditions. In this work it is proposed a combination of sensors and algorithms that can lead to the building of a navigation system for visually impaired people. Based on traditional systems that use RGB-D cameras for obstacle avoidance, it is included and combined the information of a fish-eye camera, which will give a better understanding of the user's surroundings. The combination gives robustness and reliability to the system as well as a wide field of view that allows to obtain many information from the environment. This combination of sensors is inspired by human vision where the center of the retina (fovea) provides more accurate information than the periphery, where humans have a wider field of view. The proposed system is mounted on a wearable device that provides the obstacle-free zones of the scene, allowing the planning of trajectories for people guidance.

Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an educational context such information may be deemed insufficient, it would be beneficial for both the student and the instructor to receive additional feedback about the overall development of the task. This work aims to tackle this limitation by considering the further exploitation of the information gathered by the OJ and automatically inferring feedback for both the student and the instructor. More precisely, we consider the use of learning-based schemes -- particularly, multi-instance learning (MIL) and classical machine learning formulations -- to model student behavior. Besides, explainable artificial intelligence (XAI) is contemplated to provide human-understandable feedback. The proposal has been evaluated considering a case of study comprising 2500 submissions from roughly 90 different students from a programming-related course in a computer science degree. The results obtained validate the proposal: The model is capable of significantly predicting the user outcome (either passing or failing the assignment) solely based on the behavioral pattern inferred by the submissions provided to the OJ. Moreover, the proposal is able to identify prone-to-fail student groups and profiles as well as other relevant information, which eventually serves as feedback to both the student and the instructor.

The Kinematic Theory of rapid movements, and its associated Sigma-Lognormal, model 2D spatiotemporal trajectories. It is constructed mainly as a temporal overlap of curves between virtual target points. Specifically, it uses an arc and a lognormal as primitives for the representation of the trajectory and velocity, respectively. This paper proposes developing this model, in what we call the Kinematic Theory Transform, which establishes a mathematical framework that allows further primitives to be used. Mainly, we evaluate Euler curves to link virtual target points and Gaussian, Beta, Gamma, Double-bounded lognormal, and Generalized Extreme Value functions to model the bell-shaped velocity profile. Using these primitives, we report reconstruction results with spatiotemporal trajectories executed by human beings, animals, and anthropomorphic robots., Comment: Accepted version: published on Pattern Recognition Letters [ISSN 0167-8655], v. 167, p. 181-188, (Marzo 2023)

Signature synthesis is a computation technique that generates artificial specimens which can support decision making in automatic signature verification. A lot of work has been dedicated to this subject, which centres on synthesizing dynamic and static two-dimensional handwriting on canvas. This paper proposes a framework to generate synthetic 3D on-air signatures exploiting the lognormality principle, which mimics the complex neuromotor control processes at play as the fingertip moves. Addressing the usual cases involving the development of artificial individuals and duplicated samples, this paper contributes to the synthesis of: (1) the trajectory and velocity of entirely 3D new signatures; (2) kinematic information when only the 3D trajectory of the signature is known, and (3) duplicate samples of 3D real signatures. Validation was conducted by generating synthetic 3D signature databases mimicking real ones and showing that automatic signature verifications of genuine and skilled forgeries report performances similar to those of real and synthetic databases. We also observed that training 3D automatic signature verifiers with duplicates can reduce errors. We further demonstrated that our proposal is also valid for synthesizing 3D air writing and gestures. Finally, a perception test confirmed the human likeness of the generated specimens. The databases generated are publicly available, only for research purposes, at ., Comment: Accepted Version. Published on Knowledge-Based Systems

A Ni/CeO2/ZrO2 catalyst with improved redox properties has been washcoated onto a honeycomb cordierite monolith in the form of a nonconventional alumina-catalyst layer, just a few nanometer thick. In spite of the very low active phase loading, the monolith depicts outstanding performance in dry reforming of methane, both in terms of activity, with values reaching the thermodynamic limit already at 750 C, even under extreme weight Hourly Space Velocities (WHSV 115-346 L.g-1.h-1), as well as in terms of stability during prolonged time on stream (TOS 24-48 h)., Comment: 7 pages, 3 figures, 19 references. Gold Open Access

Redox and catalytic performance in total methane oxidation of a nonostructured ceria-terbia catalyst supported on magnesia is presented and compared to that of a pure ceria catalyst supported on MgO. The investigated material, Ce0.5Tb0.5Ox (3% mol.)/MgO, features several remarkable properties: a quite low total molar loading of the two lanthanide elements, high reducibility, as well as very high oxygen storage capacity al low temperatures and higher activity than MgO-supported ceria. In terms of lanthanide atomic content the catalytic performance of Ce0.5Tb0.5Ox (3% mol.)/MgO largely improves compared to that of bulk type ceria and ceria-magnesia solid solutions. Such a behavior implies proper optimization of the usage of lanthanide elements. A second contribution to atomic economy in the catalyst design relates to the fact that the new formulation demonstrate a stabilyty in the redox and catalytic performance against very high temperature treatments. An investigation on the structure of both the fresh and high-temperature-aged catalyst at the atomic scale by means of complementary aberration corrected microscopy techniques, reveals the ocuurrence of a variety of exotic, lanthanide-containing nanostructures, which span fron isolated, atomically dispersed Ln species to nonometer-sized CeTbO2-x patches, extended CeTbO2-x bilayers and 2D CeTbO2-x nanoparticles. Nanoanalytical results evidence the mixing of the two lanthanides at atomic levels in these nanostructures. The combined effects of nanostructuring, mixing of the lanthanides at the atomic level, and interaction with the MgO oxide are the roots of the improvement in funtional, redox and catalytic properties of the novel Ce0.5Tb0.5Ox (3% mol.)/MgO catalyst., Comment: 11 pages, eight figures, 3 tables, 60 references

Supported Ni catalysts (4,5% wt%) using a Ce-Zr oxide (18/82 molar ratio and a ceria rich surface) depicting advanced redox properties, were deposited by washcoating over cordierite honeycombs (230 and 400 cpsi). FIB-STEM unveiled nanostructure details otherwise undistinguishable by conventional techniques. The catalytic performance was evaluated in the dry reforming of methane al 700-900 degree C, using a CH4:CO2 1:1 feddstock, and exploring high Weight Hourly Space Velocity (115-346 L.g-1.h-1). The structured catalyts exhibited better performance than the corresponding powers, reaching values close to rhermodynamics limits for booth reactants conversion and H2/CO ratio, from 750 dgree C, and no deactivation was observed in prolongued experiments (24-48 h). This was related to both the high catalytic efficiency after being deposited with low loading on the cordierite and the intrinsic advantages of the monolithic reactor, like preventing from the kinetic control that operates in powdered samples under high WHSV or limiting the deactivation., Comment: 10 pages, 8 figures, 3 tables, 61 references

During the pre-main-sequence evolution, Young Stellar Objects (YSOs) assemble most of their mass during the episodic accretion process. The rarely seen FUOr-type events (FUOrs) are valuable laboratories to investigate the outbursting nature of YSOs. Here, we present multi-wavelength detection of a high-amplitude eruptive source in the young open cluster VdBH 221 with an ongoing outburst, including optical to mid-infrared time series and near-infrared spectra. The initial outburst has an exceptional amplitude of $>$6.3 mag in Gaia and 4.6 mag in $K_s$, with a peak luminosity up to 16 $L_{\odot}$ and a peak mass accretion rate of 1.4 $\times$ 10$^{-5}$ $M_\odot$ yr$^{-1}$. The optical to infrared spectral energy distribution (SED) of this object is consistent with a low-mass star (0.2$M_\odot$) with a modest extinction ($A_V < 2$ mag). A 100-d delay between optical and infrared rising stages is detected, suggesting an outside-in origin of the instability. The spectroscopic features of this object reveal a self-luminous accretion disc, very similar to FU Orionis, with a low line-of-sight extinction. Most recently, there has been a gradual increase in brightness throughout the wavelength range, possibly suggesting an enhancement of the mass accretion rate., Comment: 10 pages, 8 figures

"Objective: The electrocardiogram (ECG) is currently the most widely used recording to diagnose cardiac disorders, including the most common supraventricular arrhythmia, such as atrial fibrillation (AF). However, different types of electrical disturbances, in which power-line interference (PLI) is a major problem, can mask and distort the original ECG morphology. This is a significant issue in the context of AF, because accurate characterization of fibrillatory waves (f-waves) is unavoidably required to improve current knowledge about its mechanisms. This work introduces a new algorithm able to reduce high levels of PLI and preserve, simultaneously, the original ECG morphology. Approach: The method is based on stationary wavelet transform shrinking and makes use of a new thresholding function designed to work successfully in a wide variety of scenarios. In fact, it has been validated in a general context with 48 ECG recordings obtained from pathological and non-pathological conditions, as well as in the particular context of AF, where 380 synthesized and 20 long-term real ECG recordings were analyzed. Main results: In both situations, the algorithm has reported a notably better performance than common methods designed for the same purpose. Moreover, its effectiveness has proven to be optimal for dealing with ECG recordings affected by AF, since f-waves remained almost intact after removing very high levels of noise. Significance: The proposed algorithm may facilitate a reliable characterization of the f-waves, preventing them from not being masked by the PLI nor distorted by an unsuitable filtering applied to ECG recordings with AF."

Surgical ablation (SA) is the most effective procedure to terminate atrial fibrillation (AF) in patients requiring concomitant open heart surgery. However, considering the great stress provoked in the patients heart, along with the benefits of anticipating antiarrhythmic therapeutical decisions, preoperative prediction of long term failure of the procedure is an interesting clinical challenge. Hence, the present work introduces a novel algorithm to anticipate SA outcome after one year of follow up by just analyzing the surface ECG. The method firstly extracts fibrillatory waves reflected on standard lead V1 using an adaptive QRST cancellation approach. The resulting signal is then segmented into 1 s length intervals and wavelet energy is computed for all of them. Finally, the coefficient of variation of the time series obtained for the 7th scale is computed. Analyzing 20 second length preoperative ECG excerpts from 53 persistent AF patients undergoing concomitant openheart surgery, only the proposed method reported statistically significant differences between the patients who relapsed to AF and those who maintained sinus rhythm during the follow up. The algorithm also provided values of sensitivity, specificity, and accuracy between 10 and 20% better than the well established dominant atrial frequency and fibrillatory waves amplitude, thus suggesting to be a promising predictor of AF recurrence after SA.

Although catheter ablation (CA) is still the first-line treatment for persistent atrial fibrillation (AF) patients, its limited long-term success rate has motivated clinical interest in preoperative prediction on the procedures outcome to provide optimized patient selection, limit repeated procedures, hospitalization rates, and treatment costs. To this respect, dominant frequency (DF) and amplitude of fibrillatory waves (f-waves) reflected on the ECG have provided promising results. Hence this work explores the ability of a novel set of frequency and amplitud f-waves features, such as spectral entropy (SE), spectral flatness measure (SFM), and amplitud spectrum area (AMSA), along with DF and normalized f-wave amplitude (NFWA), to improve CA outcome prediction. Despite all single indices reported statistically significant differences between patients who relapsed to AF and those who maintained sinus rhythm after a follow up of 9 months for 204 6 s-length ECG intervals extracted from 51 persistent AF patients, they obtained a limited discriminant ability ranging between 55 and 62%, which was overcome by 15 - 23% when NFWA, SE and AMSA were combined. Consequently, this combination of frequency and amplitude features of the fwaves seems to provide new insights about the atrial substrate remodeling, which could be helpful in improving preoperative CA outcome prediction.

Analysis of intra-atrial electrograms (EGMs) nowadays constitutes the most common way to gain new insights about the mechanisms triggering and maintaining atrial fibrillation (AF). However, these recordings are highly contaminated by powerline interference (PLI) due to the large amount of electrical devices operating simultaneously in the electrophysiology laboratory. To remove this perturbation, conventional notch filtering has been widely used. However, this method adds artificial fractionation to the EGMs, thus concealing their accurate interpretation. Hence, the development of novel algorithms for PLI suppression in EGMs is still an unresolved challenge. Within this context, the present work introduces the joint application of common notch filtering and Wavelet denoising for enhanced PLI removal in AF EGMs. The algorithm was validated on a set of 100 unipolar EGM signals, which were synthesized with different noise levels. Original and denoised EGMs were compared in terms of a signed correlation index (SCI), computed both in time and frequency domains. Compared with the single use of notch filtering, improvements between 4 and 15% were reached with Wavelet denoising in both domains. As a consequence, the proposed algorithm was able to efficiently reduce high levels of PLI and simultaneously preserve the original morphology of AF EGMs.

Nowadays, the electrocardiogram (ECG) is still the most widely used signal for the diagnosis of cardiac pathologies. However, this recording is often disturbed by the powerline interference (PLI), its removal being mandatory to avoid misdiagnosis. Although a broad variety of methods have been proposed for that purpose, often they substantially alter the original signal morphology or are computationally expensive. Hence, the present work introduces a simple and efficient algorithm to suppress the PLI from the ECG. Briefly, the input signal is decomposed into four Wavelet levels and the resulting coefficients are thresholded to remove the PLI estimated from the TQ intervals. The denoised ECG signal is then reconstructed by computing the inverse Wavelet transform. The method has been validated making use of fifty 10-min length clean ECG segments obtained from the MIT BIH Normal Sinus Rhythm database, which were contaminated with a sinusoidal signal of 50 Hz and variable harmonic content. Comparing the original and denoised ECG signals through a signed correlation index, improvements between 10 - 72% have been observed with respect to common adaptive notch filtering, implemented for comparison. These results suggest that the proposed method is featured by an enhanced trade-off between noise reduction and signal morphology preservation.

The relative importance of magnetic fields, turbulence, and gravity in the early phases of star formation is still not well understood. We report the first high-resolution dust polarization observations at 850 $\mu$m around the most massive clump, located at the hub of the Giant Molecular Cloud G148.24+00.41, using SCUBA-2/POL-2 at the James Clerk Maxwell Telescope. We find that the degree of polarization decreases steadily towards the denser portion of the cloud. Comparing the intensity gradients and local gravity with the magnetic field orientations, we find that local gravity plays a dominant role in driving the gas collapse as the magnetic field orientations and gravity vectors seem to point towards the dense clumps. We also find evidence of U-shaped magnetic field morphology towards a small-scale elongated structure associated with the central clump, hinting at converging accretion flows towards the clump. Our observation has resolved the massive clump into multiple substructures. We study the magnetic field properties of two regions, central clump (CC) and northeastern elongated structure (NES). Using the modified Davis-Chandrasekhar Fermi method, we determine that the magnetic field strengths of CC and NES are $\sim$24.0 $\pm$ 6.0 $\mu$G and 20.0 $\pm$ 5.0 $\mu$G, respectively. The mass-to-flux ratios are found to be magnetically transcritical/supercritical, while the Alfv$\acute{\text{e}}$n Mach number indicates a trans-Alfv$\acute{\text{e}}$nic state in both regions. These results, along with Virial analysis, suggest that at the hub of G148.24+00.41, gravitational energy has an edge over magnetic and kinetic energies., Comment: 17 pages, 23 figures

Filamentary flows toward the centre of molecular clouds have been recognized as a crucial process in the formation and evolution of stellar clusters. In this paper, we present a comprehensive observational study that investigates the gas properties and kinematics of the Giant Molecular Cloud G148.24+00.41 using the observations of CO (1-0) isotopologues. We find that the cloud is massive (10$^5$ M$_\odot$) and is one of the most massive clouds of the outer Galaxy. We identified six likely velocity coherent filaments in the cloud having length, width, and mass in the range of 14$-$38 pc, 2.5$-$4.2 pc, and (1.3$-$6.9) $\times$ 10$^3$ M$_\odot$, respectively. We find that the filaments are converging towards the central area of the cloud, and the longitudinal accretion flows along the filaments are in the range of $\sim$ 26$-$264 M$_\odot$ Myr$^{-1}$. The cloud has fragmented into 7 clumps having mass in the range of $\sim$ 260$-$2100 M$_\odot$ and average size around $\sim$ 1.4 pc, out of which the most massive clump is located at the hub of the filamentary structures, near the geometric centre of the cloud. Three filaments are found to be directly connected to the massive clump and transferring matter at a rate of $\sim$ 675 M$_\odot$ Myr$^{-1}$. The clump hosts a near-infrared cluster. Our results show that large-scale filamentary accretion flows towards the central region of the collapsing cloud is an important mechanism for supplying the matter necessary to form the central high-mass clump and subsequent stellar cluster., Comment: 21 pages, 20 figures